A qualitative look at child care selection among rural welfare-to-work participants

The welfare reforms of 1996 changed the administration of public assistance to children and families. One of the key provisions, requiring parents to join the workforce, resulted in an expanded need for child care. Prior research demonstrates that welfare recipients, particularly in rural areas, utilize informal child care arrangements. Further, recipients in rural communities face greater challenges due to a lack of transportation, geographic spread, and less access to services. This qualitative study examined the child care selection experiences of 33 welfare recipients in six rural Northern California counties. More families selected home-based than center-based child care. During their search processes, welfare recipients utilized both formal and informal routes to locate child care providers. Parents took advantage of their connections with the social service system, but also accessed their social networks for recommendations. Rural families also reported that their choices were constrained by the available programs, transportation, and by the quality of the programs they were able to access. Given the high rate of utilization of home-based arrangements among families in welfare-to-work programs, quality of care in such programs should be improved. Recommendations for increasing quality and access, as well as supports for welfare to work participants, are discussed.     

Tests for the equality of multinomial parameters

In this paper we face the problem of testing the equality of two or more parameters of a multinomial distribution. We develop a likelihood ratio test and we consider an asymptotically equivalent Pearson's statistic. Moreover we develop an exact and a randomized test. Relationships between these tests are then discussed. The behaviour of these tests is studied by simulations. Results from two known tests developed for less general situations are compared to ours.     

A combined test for differences in scale based on the interquantile range

A class of tests due to Shoemaker (Commun Stat Simul Comput 28: 189–205, 1999) for differences in scale which is valid for a variety of both skewed and symmetric distributions when location is known or unknown is considered. The class is based on the interquantile range and requires that the population variances are finite. In this paper, we firstly propose a permutation version of it that does not require the condition of finite variances and is remarkably more powerful than the original one. Secondly we solve the question of what quantile choose by proposing a combined interquantile test based on our permutation version of Shoemaker tests. Shoemaker showed that the more extreme interquantile range tests are more powerful than the less extreme ones, unless the underlying distributions are very highly skewed. Since in practice you may not know if the underlying distributions are very highly skewed or not, the question arises. The combined interquantile test solves this question, is robust and more powerful than the stand alone tests. Thirdly we conducted a much more detailed simulation study than that of Shoemaker (1999) that compared his tests to the F and the squared rank tests showing that his tests are better. Since the F and the squared rank test are not good for differences in scale, his results suffer of such a drawback, and for this reason instead of considering the squared rank test we consider, following the suggestions of several authors, tests due to Brown–Forsythe (J Am Stat Assoc 69:364–367, 1974), Pan (J Stat Comput Simul 63:59–71, 1999), O’Brien (J Am Stat Assoc 74:877–880, 1979) and Conover et al. (Technometrics 23:351–361, 1981).     

Wild adaptive trimming for robust estimation and cluster analysis

Trimming principles play an important role in robust statistics. However, their use for clustering typically requires some preliminary information about the contamination rate and the number of groups. We suggest a fresh approach to trimming that does not rely on this knowledge and that proves to be particularly suited for solving problems in robust cluster analysis. Our approach replaces the original K‐population (robust) estimation problem with K distinct one‐population steps, which take advantage of the good breakdown properties of trimmed estimators when the trimming level exceeds the usual bound of 0.5. In this setting, we prove that exact affine equivariance is lost on one hand but, on the other hand, an arbitrarily high breakdown point can be achieved by “anchoring” the robust estimator. We also support the use of adaptive trimming schemes, in order to infer the contamination rate from the data. A further bonus of our methodology is its ability to provide a reliable choice of the usually unknown number of groups.     

Patterns of Attachment and Emotional Competence in Middle Childhood

The study investigated the relationship between patterns of attachment and emotional competence at the beginning of middle childhood in a sample of 122 seven‐year‐olds. A new battery of tasks was developed in order to assess two facets of emotional competence (emotion recognition and knowledge of regulation strategies). Attachment was related to the choice of emotion regulation strategies in hypothetical situations; secure children produced the highest frequency of cognitive engagement strategies (e.g., reappraisal), and disorganized children the lowest. Insecure children produced more behavioral engagement strategies and fewer behavioral diversion ones. There was a minor effect of attachment on emotion recognition, with disorganized children scoring lower in the discrimination of facial expression. Consistent sex differences were also apparent in the direction of a female advantage in emotional competence: Girls scored higher in emotion recognition than boys, and in the regulation knowledge task, they produced fewer helpless answers and more cognitive engagement strategies.     

A multiple imputation approach to deal with the unity measure error

Unity measure errors (UME) in numerical survey data can determine serious bias in the estimates of interest. In this paper, a finite Gaussian mixture model is used to identify observations affected by UME and to robustly estimate the target parameters in presence of this type of error. In the proposed model, the mixture components are associated to the different error patterns across the variables. We follow a multiple imputation approach in a Bayesian setting that allows us to handle missing values in data. In this framework, the assessment of the uncertainty associated with both errors and missingness is based on repeatedly drawing from the predictive distribution of the true non contaminated data given the observed data. The draws are obtained through a suitable version of the data augmentation algorithm. Applications to both simulated and real data are presented.     

Using recursive algorithms for the efficient identification of smoothing spline ANOVA models

In this paper we present a unified discussion of different approaches to the identification of smoothing spline analysis of variance (ANOVA) models: (i) the “classical” approach (in the line of Wahba in Spline Models for Observational Data, 1990; Gu in Smoothing Spline ANOVA Models, 2002; Storlie et al. in Stat. Sin., 2011) and (ii) the State-Dependent Regression (SDR) approach of Young in Nonlinear Dynamics and Statistics (2001). The latter is a nonparametric approach which is very similar to smoothing splines and kernel regression methods, but based on recursive filtering and smoothing estimation (the Kalman filter combined with fixed interval smoothing). We will show that SDR can be effectively combined with the “classical” approach to obtain a more accurate and efficient estimation of smoothing spline ANOVA models to be applied for emulation purposes. We will also show that such an approach can compare favorably with kriging.     

New challenges for the welfare state: The emergence of youth unemployment regimes in Europe?

In this article we discuss the emergence of ‘youth unemployment regimes’ in Europe, that is, a set of coherent measures and policies aimed at providing state responses to the problem of unemployment and, more specifically, youth unemployment. We classify these measures and policies along two main dimensions: unemployment regulations and labour market regulations. Using original data, we show how seven European countries locate on these two dimensions as well as within the conceptual space resulting from the combination of the two dimensions. Our findings show cross‐national variations that do not fit the traditional typologies of comparative welfare studies. At the same time, however, the findings allow for reflecting upon possible patterns of convergence across European countries. In particular, we show some important similarities in terms of flexible labour market regulations. In this regard, the recent years have witnessed a trend towards a flexibilisation of the labour market, regardless of the prevailing welfare regime.     

Political,Biographical, and Cultural Consequences of Social Movements

This essay reviews recent and less recent literature on the consequences of social movements and protest activities. It focuses on three types of consequences: political, personal and biographical, and cultural. Political consequences and, in particular, policy outcomes receive most attention, as they are those which have been addresses most often by students of social movements. The review of existing work shows that the field is full of valuable works dealing with this crucial issue and is rapidly growing thanks in particular to a new wave of scholars interested in this topic. Further work should pay more attention to the unintended consequences of social movements, look also at other types of impacts, and carry more comparative analyses.     

ENDOGENOUS NETWORKS IN RANDOM POPULATION GAMES

Population learning in dynamic economies traditionally has been studied in contexts where payoff landscapes are smooth. Here, dynamic population games take place over “rugged” landscapes, where agents are uncertain about payoffs from bilateral interactions. Notably, individual payoffs from playing a binary action against everyone else are uniformly distributed over [0, 1]. This random population game leads the population to adapt over time, with agents updating both actions and partners. Agents evaluate payoffs associated to networks thanks to simple statistics of the distributions of payoffs associated to all combinations of actions performed by agents out of the interaction set. Simulations show that: (1) allowing for endogenous networks implies higher average payoff compared to static networks; (2) the statistics used to evaluate payoffs affect convergence to steady-state; and (3) for statistics MIN or MAX, the likelihood of efficient population learning strongly depends on whether agents are change-averse or not in discriminating between options delivering the same expected payoff.